ARTIFICIAL INTELLIGENCE

Paper Code: 
MCA 324C
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

The course enables the students to

  1. Apply the role of semantics of sentences and pragmatics.
  2. Analyzing work involving the design of computer programs for various application domains.
  3. Evaluate different search strategies in AI and how AI can be applied to different problems and how the Expert system helps in real life.
  4. Create and develop ideas for various applications of AI

 

Course Outcomes(COs):

Learning Outcome (at course level)

 

Learning and teaching

strategies

Assessment Strategies

CLO112. Apply knowledge representation techniques   like semantic networks, Frame system, Script etc.

CLO113. Analyze various AI Fields like Natural        Language Processing, Probability, Expert System.

CLO114. Evaluate expert system and

use of expert system application in

the real world.

CLO115. Create and develop ideas about various Applications of AI.

Interactive Lectures, Modeling, Discussions, Using research papers, student centered approach, Through Video Tutorials

Learning activities for the students:

Experiential Learning, Presentations, case based learning, Discussions, Quizzes and Assignments

 

 

  • Assignments
  • Assignments
  • Written test in classroom
  • Classroom activity
  • Continuous Assessment
  • Semester End Examination

 

 

12.00
Unit I: 
General Issues and overview of AI:

The AI problems: what is an AI technique, Characteristics of AI applications Problem Solving, Search and Control Strategies General Problem solving, Production systems, Control strategies, forward and backward chaining Exhaustive searches: Depth first Breadth first search.

 

12.00
Unit II: 
Heuristic Search Techniques:

Hill climbing, Branch and Bound technique, Best first search and A* algorithm, AND/OR Graphs, Problem reduction and AO* algorithm, Constraint Satisfaction problems Game Playing Min Max Search procedure, Alpha-Beta cutoff, Additional Refinements.

 

12.00
Unit III: 
Knowledge Representation:

First Order Predicate Calculus, Resolution Principle and Unification, Inference Mechanisms Horn’s Clauses, Semantic Networks, Frame Systems and Value Inheritance, Scripts, Conceptual Dependency AI Programming Languages Introduction to LISP, Introduction to PROLOG.

 

12.00
Unit IV: 
Natural Language Processing:

Origins and challenges of NLP – Language Modeling: Grammar-based LM, Statistical LM – Regular Expressions, Finite-State Automata – English Morphology, Tokenization, Unsmoothed N-grams, Evaluating N-grams,  Smoothing, Part-of-Speech Tagging, Issues in Part-of-Speech tagging.

Semantics and pragmatics-Requirements for representation, Syntax-Driven Semantic analysis, Semantic attachment-Word Senses, Relations between Senses.

Syntactic analysis: Context-Free Grammars, Grammar rules for English, Normal Forms for grammar – Dependency Grammar – Syntactic Parsing, and Ambiguity.

 

12.00
Unit V: 
Probability and Expert Systems:

Probabilistic Reasoning and Uncertainty, Probability theory, Bayes Theorem and Bayesian networks, Certainty Factor.

Introduction to Expert Systems, Architecture of Expert Systems, Expert System Shells, Knowledge

Acquisition, Case Studies, MYCIN, Learning, Rote Learning, Learning by Induction, explanation based learning.

 

ESSENTIAL READINGS: 
  • Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill, 3rd edition, 2009.
  • Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice Hall of India, 1st edition, 1997.
  • Winston, Patrick, Henry, “Artificial Intelligence”, Pearson Education, 3rd edition, 2004
  • Subhasree Bhattacharjee, “Artificial Intelligence for Student” Shroff Publishers and Distributors Pvt.LTD., 1st  Edition, 2016
  • Daniel Jurafsky, James H. Martin Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech, Pearson Publication, 2014.
  • Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Pythonll, First Edition, OReilly Media, 2009.

 

REFERENCES: 
  • Nils J. Nilsson, “Principles of Artificial Intelligence (Symbolic Computation / Artificial Intelligence)”, reprint edition, 2014.
  • Stuart Russell, Peter Norving, “Artificial Intelligence: A Modern Approach”, Pearson Education, 3rd edition, 2010.  
Academic Year: 
2021-22
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